Comparing the quality of human and ChatGPT feedback of students’ writing

质量(理念) 心理学 数学教育 社会心理学 认知心理学 认识论 哲学
作者
Jacob Steiss,Tamara Tate,Steve Graham,Jazmin Cruz,Michael Hébert,Jiali Wang,Youngsun Moon,Waverly Tseng,Mark Warschauer,Carol Booth Olson
出处
期刊:Learning and Instruction [Elsevier]
卷期号:91: 101894-101894 被引量:14
标识
DOI:10.1016/j.learninstruc.2024.101894
摘要

Offering students formative feedback on their writing is an effective way to facilitate writing development. Recent advances in AI (i.e., ChatGPT) may function as an automated writing evaluation tool, increasing the amount of feedback students receive and diminishing the burden on teachers to provide frequent feedback to large classes. We examined the ability of generative AI (ChatGPT) to provide formative feedback. We compared the quality of human and AI feedback by scoring the feedback each provided on secondary student essays. We scored the degree to which feedback (a) was criteria-based, (b) provided clear directions for improvement, (c) was accurate, (d) prioritized essential features, and (e) used a supportive tone. 200 pieces of human-generated formative feedback and 200 pieces of AI-generated formative feedback for the same essays. We examined whether ChatGPT and human feedback differed in quality for the whole sample, for compositions that differed in overall quality, and for native English speakers and English learners by comparing descriptive statistics and effect sizes. Human raters were better at providing high-quality feedback to students in all categories other than criteria-based. AI and humans showed differences in feedback quality based on essay quality. Feedback did not vary by language status for humans or AI. Well-trained evaluators provided higher quality feedback than ChatGPT. Considering the ease of generating feedback through ChatGPT and its overall quality, generative AI may still be useful in some contexts, particularly in formative early drafts or instances where a well-trained educator is unavailable.
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